Dhamini, V.Subathra, Y.Aruna, V.Jothi, J Salomi BackiaSathish, A.Ali, Guma2025-12-182025-12-182025-07-15Dhamini, V., Subathra, Y., Aruna, V., Jothi, J. S. B., Sathish, A., & Ali, G. (2025, May). Scalable multi-connectivity approaches for AR/VR traffic management in 5G networks. In 2025 7th International Conference on Inventive Material Science and Applications (ICIMA): 1415-1420.https://dir.muni.ac.ug/handle/20.500.12260/844This paper presents an AI-driven predictive maintenance framework for industrial machinery using IoT sensors and deep learning models to minimize downtime and optimize operational efficiency. By enabling real-time fault detection and proactive interventions, the study supports SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production) through sustainable industrial practices. While not directly health- or water-focused, its emphasis on technological innovation aligns with Uganda’s National Development Plan IV aspirations for industrialization, infrastructure modernization, and digital transformation. The approach fosters economic growth, resource efficiency, and resilience in manufacturing systems, contributing to long-term sustainable development goals.The expanding use of AR/VR applications in 5G networks needs an efficient traffic management system for reaching the network requirements concerning latency and bandwidth usage. The current network architectures show limitations during changes in traffic flow patterns which results in performance deterioration. A Scalable Multi-Connectivity Approach based on Software-Defined Networking (SDN) with Artificial Intelligence (AI) traffic balancing and Multi-Access Edge Computing (MEC) serves to improve 5G network management of AR/VR traffic. The system implementation includes the combination of AI-based load balancing and QoS-aware network slicing and SDN-based adaptive routing for distributing traffic optimally across 5G, LTE, and Wi-Fi networks. Real-time immersive experiences improve significantly through simulation results which show a reduction in AR/VR jitter by 28 % together with an average latency of 15.3 ms. The study shows that artificial intelligence-based multi-connectivity methods create successful traffic management for AR/VR applications through optimized resource utilization in networks.enMulti-access edge computing5G mobile communicationNetwork slicingQuality of serviceJitterReal-time systemsSoftware defined networkingOptimizationWireless fidelitySystem implementationOther